Machine Learning Engineer, Answers Relevance

Accountabilities

• Design, develop, deploy, and optimize large-scale machine learning systems, including information retrieval, ranking, embeddings, recommendation systems, and LLM-powered features.

• Lead machine learning projects from ideation through production, ensuring solutions deliver measurable improvements in answer relevance, user experience, and business outcomes.

• Develop and improve retrieval and ranking models that surface useful, contextually relevant information from large-scale content collections.

• Build and optimize retrieval-augmented generation (RAG) systems, foundational retriever models, and other AI-powered features that improve the quality and usefulness of generated answers.

• Explore and implement advanced AI approaches, including large-scale recommendation systems, agentic AI, multi-agent orchestration, tool calling, and model context protocol (MCP) integrations where appropriate.

• Work with open-weight language models, including models such as Qwen and Llama, applying prompt engineering, fine-tuning, post-training, and prompt optimization techniques as needed.

• Improve model grounding, instruction following, and factual reliability while reducing hallucinations and strengthening retrieval and generation quality.

• Evaluate new machine learning methods and establish robust evaluation frameworks, feedback loops, product success metrics, and human-review processes.

• Optimize production LLM systems for latency, caching, cost efficiency, scalability, and reliability while implementing safeguards against prompt injection and unsafe inputs.

• Collaborate with Product, Data Science, and Engineering partners to define technical direction, prioritize initiatives, and shape product and engineering roadmaps.

• Build and maintain reliable ML infrastructure, including data pipelines, feature engineering workflows, model training and serving systems, and production monitoring.

• Write high-quality, maintainable production code with appropriate testing, evaluation, and monitoring practices.

• Mentor fellow engineers, share technical expertise, and contribute to long-term machine learning strategy and planning.

Requirements:

• 4–12 years of experience building, deploying, and operating machine learning systems in production.

• Broad understanding of machine learning fundamentals and demonstrated ability to apply them to practical, production-scale problems.

• Experience developing and productionizing machine learning models using frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.

• Strong Python programming skills and experience writing reliable, production-quality code, including testing, evaluation, and monitoring. Golang experience is a plus.

• Experience with core machine learning systems and workflows, including data pipelines, feature engineering, model training, inference, serving, and production monitoring.

• Familiarity with one or more relevant areas, such as recommendation systems, search, content understanding, large-scale ML infrastructure, retrieval and ranking, embeddings, or foundational retriever models.

• Experience with LLM-based applications, RAG systems, retrieval techniques, LLM evaluation, or production language model features is advantageous.

• Knowledge of open-weight language models, prompt engineering, fine-tuning, post-training, or prompt optimization is beneficial.

• Familiarity with agentic AI, tool calling, multi-agent systems, or orchestration frameworks is a plus.

• Understanding of model quality evaluation, hallucination reduction, grounding, instruction following, and responsible AI practices is desirable.

• Experience optimizing large-scale AI systems for latency, caching, cost, scalability, or reliability is an advantage.

• Strong analytical and problem-solving abilities, with the capacity to assess new approaches and translate technical findings into practical improvements.

• Excellent communication and collaboration skills, with a track record of working across disciplines and delivering measurable product impact.

• Ability to take ownership of technical projects, contribute to architectural decisions, and help guide the development of scalable ML solutions.

• Willingness to mentor colleagues and contribute to engineering best practices and long-term technical planning.

• Must be based in the United States and authorized to work in the country, subject to the hiring organization's employment eligibility requirements.

Benefits:

• Salary: Annual base salary range of $216,700–$322,000 USD, depending on role level, relevant experience, skills, qualifications, and other job-related factors.

• Equity compensation: Eligibility for restricted stock units (RSUs), with additional commission eligibility depending on the position offered.

• Healthcare coverage: Comprehensive healthcare benefits, including medical, dental, and vision insurance.

• Retirement savings: 401(k) plan with employer matching contributions.

• Income protection: Income replacement programs for eligible employees.

• Flexible time off: Vacation benefits and paid volunteer time off.

• Parental leave: Generous paid parental leave.

• Family support: Family planning benefits and resources.

• Mental health and wellbeing: Mental health support, coaching, and related wellbeing resources.

• Professional development: Benefits and resources to support continuous learning and career growth.

• Flexible work support: Global benefits designed to accommodate different lifestyles, including workspace and caregiving support.

• Remote work: Remote opportunity within the United States.

• Inclusive workplace: Commitment to equal employment opportunity, accessibility, and reasonable accommodations throughout the recruitment process.